[29]:
xxxxxxxxxx# This Python 3 environment comes with many helpful analytics libraries installed# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python# For example, here's several helpful packages to loadimport numpy as np # linear algebraimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)# Input data files are available in the read-only "../input/" directory# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directoryimport osfor dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename))# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session/kaggle/input/digit-recognizer/sample_submission.csv /kaggle/input/digit-recognizer/train.csv /kaggle/input/digit-recognizer/test.csv
[30]:
df = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")[31]:
xxxxxxxxxxdf.sample()[31]:
| label | pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | ... | pixel774 | pixel775 | pixel776 | pixel777 | pixel778 | pixel779 | pixel780 | pixel781 | pixel782 | pixel783 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19970 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 rows × 785 columns
[32]:
xxxxxxxxxxdf.head()[32]:
| label | pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | ... | pixel774 | pixel775 | pixel776 | pixel777 | pixel778 | pixel779 | pixel780 | pixel781 | pixel782 | pixel783 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 785 columns
[33]:
xxxxxxxxxximport matplotlib.pyplot as plt[34]:
xxxxxxxxxxplt.imshow(df.iloc[8525,1:].values.reshape(28,28))[34]:
<matplotlib.image.AxesImage at 0x7a9f67ecee60>
[35]:
xxxxxxxxxxX = df.iloc[:,1:]y = df.iloc[:,0][36]:
xxxxxxxxxxfrom sklearn.model_selection import train_test_splitX_train,X_test, y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state = 42)[37]:
xxxxxxxxxxX_train.shape[37]:
(33600, 784)
[38]:
xxxxxxxxxxfrom sklearn.neighbors import KNeighborsClassifier[39]:
xxxxxxxxxxknn = KNeighborsClassifier()[40]:
xxxxxxxxxxknn.fit(X_train,y_train)[40]:
KNeighborsClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier()
[41]:
xxxxxxxxxxy_pred = knn.predict(X_test)[42]:
xxxxxxxxxxfrom sklearn.metrics import accuracy_scoreaccuracy_score(y_test, y_pred)[42]:
0.9648809523809524
[43]:
xxxxxxxxxxfrom sklearn.preprocessing import StandardScalerscaler = StandardScaler()[44]:
xxxxxxxxxxX_train = scaler.fit_transform(X_train)X_test = scaler.transform(X_test)x
#PCAfrom sklearn.decomposition import PCApca = PCA(n_components = 100)x
X_train_trf = pca.fit_transform(X_train)X_test_trf = pca.transform(X_test)[47]:
xxxxxxxxxxX_train_trf.shape[47]:
(33600, 100)
x
knn = KNeighborsClassifier()x
knn.fit(X_train_trf,y_train)[49]:
KNeighborsClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier()
x
y_pred = knn.predict(X_test_trf)x
accuracy_score(y_test,y_pred)[51]:
0.9544047619047619
x
for i in range(1,785): pca = PCA(n_components = i) X_train_trf = pca.fit_transform(X_train) X_test_trf = pca.transform(X_test) knn = KNeighborsClassifier() knn.fit(X_train_trf,y_train) y_pred = knn.predict(X_test_trf) print(accuracy_score(y_test,y_pred))0.25761904761904764 0.32404761904761903 0.5103571428571428 0.6666666666666666 0.7377380952380952 0.8227380952380953 0.843452380952381 0.8723809523809524 0.8864285714285715 0.9053571428571429 0.9117857142857143 0.9183333333333333 0.9279761904761905 0.9332142857142857 0.9376190476190476 0.9384523809523809 0.9403571428571429 0.940952380952381 0.9425 0.9435714285714286 0.9435714285714286 0.9442857142857143 0.9444047619047619 0.9448809523809524 0.9478571428571428 0.9484523809523809 0.9475 0.9466666666666667 0.9486904761904762 0.9510714285714286 0.9513095238095238 0.9523809523809523 0.9510714285714286 0.950952380952381 0.9496428571428571 0.9517857142857142 0.9513095238095238 0.9530952380952381 0.9533333333333334 0.950952380952381 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x
pca = PCA(n_components = 2)X_train_trf = pca.fit_transform(X_train)X_test_trf = pca.transform(X_test)[54]:
xxxxxxxxxxX_train_trf[54]:
array([[-2.71864483, -0.48981016],
[-0.67698414, -6.75405987],
[-3.03320724, 6.50918599],
...,
[ 2.14883779, 0.78075924],
[ 1.05955416, 0.94782655],
[17.70259619, 1.96190349]])[55]:
xxxxxxxxxximport plotly.express as pxy_train_trf = y_train.astype(str)fig = px.scatter(x = X_train_trf[:,0], y = X_train_trf[:,1], color = y_train_trf, color_discrete_sequence = px.colors.qualitative.G10 )fig.show()/opt/conda/lib/python3.10/site-packages/plotly/express/_core.py:2065: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning. sf: grouped.get_group(s if len(s) > 1 else s[0])
x
pca = PCA(n_components = 3)X_train_trf = pca.fit_transform(X_train)X_test_trf = pca.transform(X_test)[57]:
xxxxxxxxxxy_train_trf = y_train.astype(str)fig = px.scatter_3d(df, x = X_train_trf[:,0], y = X_train_trf[:,1], z = X_train_trf[:,2], color = y_train_trf)fig.update_layout(margin = dict(l=20, r=20, t=20, b=20))fig.show()/opt/conda/lib/python3.10/site-packages/plotly/express/_core.py:2065: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
x
pca.explained_variance_ #Eigen Values[58]:
array([40.67111198, 29.17023399, 26.74459561])
x
pca.components_.shape #Eigen Vectors[60]:
(3, 784)
[ ]:
xxxxxxxxxxpca.explained_variance_ratio_[61]:
array([0.05785192, 0.0414927 , 0.03804239])
[ ]:
x
pca = PCA(n_components = None)X_train_trf = pca.fit_transform(X_train)X_test_trf = pca.transform(X_test)[ ]:
xxxxxxxxxxpca.explained_variance_.shape[63]:
(784,)
[ ]:
pca.components_.shape [64]:
(784, 784)
x
np.cumsum(pca.explained_variance_ratio_)[65]:
array([0.05785192, 0.09934462, 0.13738701, 0.16704964, 0.19286525,
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0.98116567, 0.98130745, 0.98144769, 0.98158734, 0.98172653,
0.98186487, 0.9820027 , 0.98213961, 0.98227604, 0.98241149,
0.98254653, 0.98268118, 0.98281498, 0.98294806, 0.98307997,
0.98321127, 0.98334209, 0.98347147, 0.98360011, 0.98372841,
0.98385617, 0.983983 , 0.98410924, 0.98423434, 0.98435884,
0.98448278, 0.98460617, 0.98472912, 0.984852 , 0.98497324,
0.98509422, 0.98521456, 0.98533478, 0.98545394, 0.9855726 ,
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0.98626975, 0.98638343, 0.9864965 , 0.98660857, 0.98672004,
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0.99999752, 1. , 1. , 1. , 1. ,
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1. , 1. , 1. , 1. , 1. ,
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1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. , 1. ,
1. , 1. , 1. , 1. ])[ ]:
plt.plot(np.cumsum(pca.explained_variance_ratio_))[66]:
[<matplotlib.lines.Line2D at 0x7a9f641185e0>]
[ ]:
- __notebook_source__.ipynb